Large data sets may exist in various sizes and organizational structures. With big data comprising data sets as large as ever, the volume of data collected incident to the increased popularity of online and electronic transactions continues to grow. For example, billions of records (also referred to as rows) and hundreds of thousands of columns worth of data may populate a single table. Users and business processes may interact with the large data sets in a variety of circumstances. For example, users and business processes may interact with and process large data sets across multiple servers in a server cluster. During the processing of large data sets, each server may define a set number of processing threads to aid in the data processing. Due to the defined number of processing threads, each server may be under-utilized and/or over-utilized depending on the performance of data transfer services, downstream applications, and each server. Typically, each server is manually monitored to validate the status of the data processing and to avoid system failures.